The coregistration of heterogeneous geospatial images is useful in various remote sensing applications. Since the number of available data increases and the resolution improves, it is interesting to have an approach as automated, fast, robust and accurate as possible. In this paper, we present a solution based on optical-flow computation. This algorithm called GeFolki allows the registration of images in a non-parametric and dense way. GeFolki is based on a local method of optical flow derived from the Lucas-Kanade algorithm, with a multi-scale implementation, and a specific filtering including rank filtering, rolling guidance filtering and local contrast inversion. The efficiency of our coregistration chain is shown on radar, LIDAR and optical images on Remningstorp forest in Sweden. An analysis of the relevant parameters is investigated for several scenarios. Finally, we demonstrate the accuracy of our coregistration by proposing specific metrics for LIDAR/radar coregistration, and optics/radar coregistration.
In this paper we present a new approach for semantic recognition in the context of robotics. When a robot evolves in its environment, it gets 3D information given either by its sensors or by its own motion through 3D reconstruction. Our approach uses (i) 3D-coherent synthesis of scene observations and (ii) mix them in a multi-view framework for 3D labeling. (iii) This is efficient locally (for 2D semantic segmentation) and globally (for 3D structure labeling). This allows to add semantics to the observed scene that goes beyond simple image classification, as shown on challenging datasets such as SUNRGBD or the 3DRMS Reconstruction Challenge. * Computed at low resolution (224x224) as in [23] on the contrary of all other results computed at native resolution. **We also test a High Definition strategy, cropping 224x244 patches at original resolution instead of warping image.
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